IDEAS home Printed from https://ideas.repec.org/a/taf/applec/v42y2010i3p389-401.html
   My bibliography  Save this article

R&D intensity, firm performance and the identification of the threshold: fresh evidence from the panel threshold regression model

Author

Listed:
  • Ming-Liang Yeh
  • Hsiao-Ping Chu
  • Peter Sher
  • Yi-Chia Chiu

Abstract

This article tests whether there is an optimal level of research and development (R&D) intensity at which point a firm is able to maximize its performance. An advanced panel threshold regression model is employed to investigate the panel threshold effect of R&D intensity on firm performance among publicly traded Taiwan information technology and electronic firms. The results confirm that a single-threshold effect does exist and show an inverted-U correlation between R&D intensity and firm performance. This article demonstrates that it is possible to identify the definitive level beyond which a further increase in R&D expenditure does not yield proportional rewards. Some important policy implications emerge from the findings.

Suggested Citation

  • Ming-Liang Yeh & Hsiao-Ping Chu & Peter Sher & Yi-Chia Chiu, 2010. "R&D intensity, firm performance and the identification of the threshold: fresh evidence from the panel threshold regression model," Applied Economics, Taylor & Francis Journals, vol. 42(3), pages 389-401.
  • Handle: RePEc:taf:applec:v:42:y:2010:i:3:p:389-401
    DOI: 10.1080/00036840701604487
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/00036840701604487
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/00036840701604487?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lawrence G. Franko, 1989. "Global corporate competition: Who's winning, who's losing, and the R&D factor as one reason why," Strategic Management Journal, Wiley Blackwell, vol. 10(5), pages 449-474, September.
    2. Thornton, Daniel L & Batten, Dallas S, 1985. "Lag-Length Selection and Tests of Granger Causality between Money and Income," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 17(2), pages 164-178, May.
    3. Mattias Villani, 2001. "Fractional Bayesian Lag Length Inference in Multivariate Autoregressive Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 22(1), pages 67-86, January.
    4. Rafael Llorca Vivero, 2002. "The impact of process innovations on firm's productivity growth: the case of Spain," Applied Economics, Taylor & Francis Journals, vol. 34(8), pages 1007-1016.
    5. Bruce E. Hansen, 2000. "Sample Splitting and Threshold Estimation," Econometrica, Econometric Society, vol. 68(3), pages 575-604, May.
    6. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
    7. Karel O. Cool & Dan Schendel, 1987. "Strategic Group Formation and Performance: The Case of the U.S. Pharmaceutical Industry, 1963--1982," Management Science, INFORMS, vol. 33(9), pages 1102-1124, September.
    8. McCutchen, William Jr., 1993. "Estimating the impact of the R&D tax credit on strategic groups in the pharmaceutical industry," Research Policy, Elsevier, vol. 22(4), pages 337-351, August.
    9. David J Ravenscraft & William F Long, 1993. "LBOs, Debt And R&D Intensity," Working Papers 93-3, Center for Economic Studies, U.S. Census Bureau.
    10. Wakelin, Katharine, 2001. "Productivity growth and R&D expenditure in UK manufacturing firms," Research Policy, Elsevier, vol. 30(7), pages 1079-1090, August.
    11. Hansen, Bruce E, 1996. "Inference When a Nuisance Parameter Is Not Identified under the Null Hypothesis," Econometrica, Econometric Society, vol. 64(2), pages 413-430, March.
    12. Hall, Bronwyn H. & Mairesse, Jacques, 1995. "Exploring the relationship between R&D and productivity in French manufacturing firms," Journal of Econometrics, Elsevier, vol. 65(1), pages 263-293, January.
    13. Sterlacchini, Alessandro, 1999. "Do innovative activities matter to small firms in non-R&D-intensive industries? An application to export performance," Research Policy, Elsevier, vol. 28(8), pages 819-832, November.
    14. Jesús Gonzalo & Jean‐Yves Pitarakis, 2002. "Lag length estimation in large dimensional systems," Journal of Time Series Analysis, Wiley Blackwell, vol. 23(4), pages 401-423, July.
    15. John E. Ettlie, 1998. "R&D and Global Manufacturing Performance," Management Science, INFORMS, vol. 44(1), pages 1-11, January.
    16. Hansen, Bruce E., 1999. "Threshold effects in non-dynamic panels: Estimation, testing, and inference," Journal of Econometrics, Elsevier, vol. 93(2), pages 345-368, December.
    17. Branch, Ben, 1974. "Research and Development Activity and Profitability: A Distributed Lag Analysis," Journal of Political Economy, University of Chicago Press, vol. 82(5), pages 999-1011, Sept./Oct.
    18. Omer Ozcicek & W. DOUGLAS McMILLIN, 1999. "Lag length selection in vector autoregressive models: symmetric and asymmetric lags," Applied Economics, Taylor & Francis Journals, vol. 31(4), pages 517-524.
    19. Hall, Bronwyn H. & Oriani, Raffaele, 2006. "Does the market value R&D investment by European firms? Evidence from a panel of manufacturing firms in France, Germany, and Italy," International Journal of Industrial Organization, Elsevier, vol. 24(5), pages 971-993, September.
    20. Del Monte, Alfredo & Papagni, Erasmo, 2003. "R&D and the growth of firms: empirical analysis of a panel of Italian firms," Research Policy, Elsevier, vol. 32(6), pages 1003-1014, June.
    21. Tassey, Gregory, 1983. "Competitive strategies and performance in technology-based industries," Journal of Economics and Business, Elsevier, vol. 35(1), pages 21-40.
    22. Lin, Bou-Wen & Lee, Yikuan & Hung, Shih-Chang, 2006. "R&D intensity and commercialization orientation effects on financial performance," Journal of Business Research, Elsevier, vol. 59(6), pages 679-685, June.
    23. Kiyohiko Ito & Vladimir Pucik, 1993. "Abstract," Strategic Management Journal, Wiley Blackwell, vol. 14(1), pages 61-75, January.
    24. Karel Cool & Dan Schendel, 1988. "Performance differences among strategic group members," Strategic Management Journal, Wiley Blackwell, vol. 9(3), pages 207-223, May.
    25. Inmaculada Martinez-Zarzoso & Celestino Suarez-Burguet, 2000. "The determinants of trade performance: influence of R&D on export flows," Applied Economics, Taylor & Francis Journals, vol. 32(15), pages 1939-1946.
    26. Robert B. Davies, 2002. "Hypothesis testing when a nuisance parameter is present only under the alternative: Linear model case," Biometrika, Biometrika Trust, vol. 89(2), pages 484-489, June.
    27. Gary Erickson & Robert Jacobson, 1992. "Gaining Comparative Advantage Through Discretionary Expenditures: The Returns to R&D and Advertising," Management Science, INFORMS, vol. 38(9), pages 1264-1279, September.
    28. Connolly, Robert A & Hirschey, Mark, 1984. "R&D, Market Structure, and Profits: A Value-Based Approach," The Review of Economics and Statistics, MIT Press, vol. 66(4), pages 682-686, November.
    29. Levin, Andrew & Lin, Chien-Fu & James Chu, Chia-Shang, 2002. "Unit root tests in panel data: asymptotic and finite-sample properties," Journal of Econometrics, Elsevier, vol. 108(1), pages 1-24, May.
    30. Jonathan P. O'Brien, 2003. "The capital structure implications of pursuing a strategy of innovation," Strategic Management Journal, Wiley Blackwell, vol. 24(5), pages 415-431, May.
    31. Bronwyn H. HALL & Raffaele ORIANI, 2004. "Does the Market Value R&D Investment by European Firms? Evidence from a Panel of Manufacturing Firms in France," Economics Working Papers ECO2004/13, European University Institute.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Chaoyi & Pinar, Mehmet & Stengos, Thanasis, 2021. "Determinants of renewable energy consumption: Importance of democratic institutions," Renewable Energy, Elsevier, vol. 179(C), pages 75-83.
    2. Yosra Saidi & Mohamed Ali Labidi & Anis Ochi, 2024. "Economic Growth and Extreme Poverty in Sub-Saharan African Countries: Non-Linearity and Governance Threshold Effect," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(2), pages 7819-7851, June.
    3. Chen, Yiqi & Ibhagui, Oyakhilome W., 2019. "R&D-firm performance nexus: New evidence from NASDAQ listed firms," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    4. Dieter Nautz & Juliane Scharff, 2012. "Inflation and relative price variability in the euro area: evidence from a panel threshold model," Applied Economics, Taylor & Francis Journals, vol. 44(4), pages 449-460, February.
    5. John D. Levendis, 2018. "Time Series Econometrics," Springer Texts in Business and Economics, Springer, number 978-3-319-98282-3, December.
    6. Hajamini, Mehdi & Falahi, Mohammad Ali, 2018. "Economic growth and government size in developed European countries: A panel threshold approach," Economic Analysis and Policy, Elsevier, vol. 58(C), pages 1-13.
    7. Yu-Shan Chen & Chun-Yu Shih & Ching-Hsun Chang, 2013. "Patents and market value in the U.S. pharmaceutical industry: new evidence from panel threshold regression," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(2), pages 161-176, November.
    8. Chen, Chaoyi & Pinar, Mehmet & Stengos, Thanasis, 2020. "Renewable energy consumption and economic growth nexus: Evidence from a threshold model," Energy Policy, Elsevier, vol. 139(C).
    9. Qiang Wang & Ting Yang & Rongrong Li & Xiaowei Wang, 2023. "Reexamining the impact of foreign direct investment on carbon emissions: does per capita GDP matter?," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-18, December.
    10. Feng-Li Lin & Tsangyao Chang, 2011. "Does debt affect firm value in Taiwan? A panel threshold regression analysis," Applied Economics, Taylor & Francis Journals, vol. 43(1), pages 117-128.
    11. Wang, Qiang & Hu, Sailan & Li, Rongrong, 2024. "Could information and communication technology (ICT) reduce carbon emissions? The role of trade openness and financial development," Telecommunications Policy, Elsevier, vol. 48(3).
    12. Vansteenkiste, Isabel & Nickel, Christiane, 2008. "Fiscal policies, the current account and Ricardian equivalence," Working Paper Series 935, European Central Bank.
    13. Afi Etonam Adetou & Komlan Fiodendji, 2019. "Finance, Institutions, Remittances and Economic growth: New Evidence from a Dynamic Panel Threshold Analysis," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 9(2), pages 1-4.
    14. Seleteng, Monaheng & Bittencourt, Manoel & van Eyden, Reneé, 2013. "Non-linearities in inflation–growth nexus in the SADC region: A panel smooth transition regression approach," Economic Modelling, Elsevier, vol. 30(C), pages 149-156.
    15. Martinez Oscar & Olmo Jose, 2012. "A Nonlinear Threshold Model for the Dependence of Extremes of Stationary Sequences," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(3), pages 1-39, September.
    16. Akinlo Taiwo & Idachaba Daniel Adukwu, 2024. "Insurance and economic growth in sub-Saharan Africa: Institutional quality threshold effect," Economic Annals, Faculty of Economics and Business, University of Belgrade, vol. 69(241), pages 7-39, April – J.
    17. Yang Song & Dayu Liu & Qiaoru Wang, 2021. "The dual-financial-threshold effect in the “club convergence” of economic growth: a dynamic panel threshold model," Empirical Economics, Springer, vol. 61(5), pages 2713-2737, November.
    18. Alessio Ciarlone, 2019. "The relationship between financial development and growth: the case of emerging Europe," Questioni di Economia e Finanza (Occasional Papers) 521, Bank of Italy, Economic Research and International Relations Area.
    19. Krambia-Kapardis Maria & Stylianou Ioanna & Demetriou Salomi, 2022. "Nonlinear nexus between corruption and tourism arrivals: a global analysis," Empirical Economics, Springer, vol. 63(4), pages 1997-2024, October.
    20. Philip Kostov & John Lingard, 2004. "Regime-switching Vector Error Correction Model (VECM) analysis of UK meat consumption," Econometrics 0409007, University Library of Munich, Germany.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:applec:v:42:y:2010:i:3:p:389-401. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEC20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.